ResampleWithDistributionTransform

class ResampleWithDistributionTransform(in_column: str, distribution_column: str, inplace: bool = True, out_column: Optional[str] = None)

Bases: etna.transforms.base.IrreversiblePerSegmentWrapper

ResampleWithDistributionTransform resamples the given column using the distribution of the other column.

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Init ResampleWithDistributionTransform.

Parameters

• in_column (str) – name of column to be resampled

• distribution_column (str) – name of column to obtain the distribution from

• inplace (bool) –

  • if True, apply resampling inplace to in_column,

  • if False, add transformed column to dataset

• out_column (Optional[str]) – name of added column. If not given, use self.__repr__()

Inherited-members

Methods

fit(ts)	Fit the transform.
fit_transform(ts)	Fit and transform TSDataset.
get_regressors_info()	Return the list with regressors created by the transform.
inverse_transform(ts)	Inverse transform TSDataset.
load(path)	Load an object.
params_to_tune()	Get grid for tuning hyperparameters.
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.
transform(ts)	Transform TSDataset inplace.

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.missing_values.resample.ResampleWithDistributionTransform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) –

Return type

etna.transforms.missing_values.resample.ResampleWithDistributionTransform

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Return type

List[str]